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Scene segmentation without labeling by combining images and LiDAR with Drive&Segment

Scene segmentation without labeling by combining images and LiDAR with Drive&Segment


Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation

arXiv paper abstract https://arxiv.org/abs/2203.11160



... investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, equipped with cameras and LiDAR sensors, drive around a city.


... First, ... propose ... cross-modal unsupervised learning of semantic image segmentation by leveraging synchronized LiDAR and image data.


... method is the use of an object proposal module that analyzes the LiDAR point cloud to obtain proposals for spatially consistent objects.


Second, ... show that these 3D object proposals can be aligned with the input images and reliably clustered into semantically meaningful pseudo-classes.


Finally, ... develop a cross-modal distillation approach that leverages image data partially annotated with the resulting pseudo-classes to train a transformer-based model for image semantic segmentation.


... without any finetuning, and demonstrate significant improvements compared to the current state of the art ...



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